{"id":"W2098174433","doi":"10.1016/j.eswa.2013.06.043","title":"Securing high resolution grayscale facial captures using a blockwise coevolutionary GA","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grayscale; Digital watermarking; Computer science; Artificial intelligence; Pixel; Crossover; Block (permutation group theory); Embedding; Pattern recognition (psychology); Biometrics; Computer vision; Evolutionary computation; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002799549,0.0003728184,0.0003889307,0.0003043312,0.0001633102,0.0002800376,0.0003821445,0.000549347,0.0009942316],"category_scores_gemma":[0.0006482827,0.0001913162,0.0003747744,0.0002610315,0.0003002208,0.0003319818,0.0004910921,0.0003464426,0.000260919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303134,"about_ca_system_score_gemma":0.0002486965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137917,"about_ca_topic_score_gemma":0.002209313,"domain_scores_codex":[0.999873,0.00002071011,0.000007173404,0.00002975977,0.0000532622,0.00001599499],"domain_scores_gemma":[0.9998339,0.00005972489,0.00002164029,0.00003101754,0.00004468183,0.000008895568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001675373,0.00007637052,0.001091925,0.00008343702,0.00008806102,0.0002021363,0.0001586748,0.321688,0.3870832,0.006687176,0.0006705808,0.2820029],"study_design_scores_gemma":[0.000006270335,0.00008086671,0.0004498256,0.000005169133,0.00001715615,0.0001033779,0.000009215469,0.9826376,0.01567016,0.0004568832,0.0005563719,0.000007101698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1096509,0.0001785162,0.8873475,0.00006627355,0.00003936649,0.00004712199,0.00001690234,0.0002862696,0.002367156],"genre_scores_gemma":[0.6137685,0.0002212897,0.3795962,0.00008430266,0.00001717074,0.00007088618,0.00004486321,0.00006153391,0.006135232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00137917,"threshold_uncertainty_score":0.003326058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213866885917137,"score_gpt":0.2369991695674705,"score_spread":0.2248605007082992,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}